Predictive Analytics for Electrical Grid Equipment Failure
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Solution Overview
Problem
Existing electrical grid monitoring systems fail to accurately predict equipment failures and cascading disruptions due to their reliance on post-event diagnostic tests, which are unable to identify the combination of parameters leading to failures in real-time.
Innovation Solution
Implementing a computer-implemented method and system for predictive analytics that aggregates and analyzes events from electrical grids using time-slice parameters and correlation techniques to recognize patterns, predict future events, and adjust accuracy parameters for improved forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic tests are used to monitor equipment performance, then equipment failure can be detected, but failure prediction capability is insufficient because tests report only after failure occurs
Solution Approach 1:
The system performs preliminary analysis of multiple parameters (temperature, humidity, load, age) before equipment failure occurs. By continuously monitoring and analyzing these parameters in advance, the system predicts potential failures before they happen, rather than detecting them only after failure occurs through diagnostic tests.
Solution Approach 2:
The system dynamically adjusts the monitoring and analysis process based on real-time parameter changes. Instead of static diagnostic tests, the system continuously updates predictions as parameters change, allowing adaptive failure prediction that responds to evolving equipment conditions.
2Loss of information
If multiple parameters are monitored to determine equipment failure causes, then comprehensive analysis is achieved, but system complexity increases making it difficult to identify parameter combinations leading to failure
Solution Approach 1:
The system segments the complex multi-parameter analysis into distinct monitoring components for each parameter (temperature, humidity, load, age). Each parameter is monitored and analyzed separately, then the results are integrated to determine failure causes. This segmentation makes the complex analysis manageable and systematic.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes multiple parameters and identifies their combinations leading to failure. This intermediary layer simplifies the complex relationships between parameters by systematically analyzing their interactions and presenting integrated failure cause information.
3Measurement precision
If historical and real-time measurements are used for prediction, then prediction accuracy is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system merges historical measurement data with real-time measurements into a unified analysis framework. By combining these data sources, the system leverages both past trends and current conditions to improve prediction accuracy, while the integrated approach manages the complexity of processing multiple data streams.
Data Source
AI summary
Systems and methods for performing predictive analytics in an electrical grid network are disclosed. In one example of the disclosed technology, a method comprises aggregating a plurality of events from an electrical grid network, analyzing the plurality of events to recognize at least one event pattern, serializing at least one of the event patterns in a database, and predicting a future event pattern based on a correlation of the plurality of event patterns.